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<h4 id="TECHNICAL-ARTICLES"><a href="#TECHNICAL-ARTICLES" class="headerlink" title="TECHNICAL ARTICLES"></a><strong><em>TECHNICAL ARTICLES</em></strong></h4><p><strong><em>Quick Links:</em></strong></p>
<ul>
<li><a href="https://github.com/createmomo" target="_blank" rel="external">[Github] https://github.com/createmomo</a></li>
<li><a href="https://createmomo.github.io/2019/01/07/Probabilistic-Graphical-Models-Revision-Notes/#tableofcontents">[2019] Probabilistic Graphical Models Revision Notes</a></li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#tableofcontents">[2018] Super Machine Learning Revision Notes</a></li>
<li>[2017] CRF Layer on the Top of BiLSTM<ul>
<li><a href="https://createmomo.github.io/2017/09/12/CRF_Layer_on_the_Top_of_BiLSTM_1/">CRF Layer on the Top of BiLSTM - 1</a> Outline and Introduction</li>
<li><a href="https://createmomo.github.io/2017/09/23/CRF_Layer_on_the_Top_of_BiLSTM_2/">CRF Layer on the Top of BiLSTM - 2</a> CRF Layer (Emission and Transition Score)</li>
<li><a href="https://createmomo.github.io/2017/10/08/CRF-Layer-on-the-Top-of-BiLSTM-3/">CRF Layer on the Top of BiLSTM - 3</a> CRF Loss Function</li>
<li><a href="https://createmomo.github.io/2017/10/17/CRF-Layer-on-the-Top-of-BiLSTM-4/">CRF Layer on the Top of BiLSTM - 4</a> Real Path Score</li>
<li><a href="https://createmomo.github.io/2017/11/11/CRF-Layer-on-the-Top-of-BiLSTM-5/">CRF Layer on the Top of BiLSTM - 5</a> The Total Score of All the Paths</li>
<li><a href="https://createmomo.github.io/2017/11/24/CRF-Layer-on-the-Top-of-BiLSTM-6/">CRF Layer on the Top of BiLSTM - 6</a> Infer the Labels for a New Sentence</li>
<li><a href="https://createmomo.github.io/2017/12/06/CRF-Layer-on-the-Top-of-BiLSTM-7/">CRF Layer on the Top of BiLSTM - 7</a> Chainer Implementation Warm Up</li>
<li><a href="https://createmomo.github.io/2017/12/07/CRF-Layer-on-the-Top-of-BiLSTM-8/">CRF Layer on the Top of BiLSTM - 8</a> Demo Code<br><img src="/2019/07/18/Table-of-Contents/search.png" alt="Searching..."></li>
</ul>
</li>
</ul>
<p><strong><em>Detailed Links:</em></strong><br><strong>(Last Updated: 2019.09.15) <a href="https://createmomo.github.io/2019/01/07/Probabilistic-Graphical-Models-Revision-Notes/#tableofcontents">Probabilistic Graphical Models Revision Notes</a></strong></p>
<ul>
<li><strong><a href="https://createmomo.github.io/2019/01/07/Probabilistic-Graphical-Models-Revision-Notes/#representations">Representations</a></strong><ul>
<li><strong><a href="https://createmomo.github.io/2019/01/07/Probabilistic-Graphical-Models-Revision-Notes/#bayesian_network">Bayesian Network (directed graph)</a></strong><ul>
<li><a href="https://createmomo.github.io/2019/01/07/Probabilistic-Graphical-Models-Revision-Notes/#defination">Defination</a></li>
<li><a href="https://createmomo.github.io/2019/01/07/Probabilistic-Graphical-Models-Revision-Notes/#reasoning_patterns_in_bayesian_network">Reasoning Patterns in Bayesian Network</a></li>
<li><a href="https://createmomo.github.io/2019/01/07/Probabilistic-Graphical-Models-Revision-Notes/#flow_of_probabilistic_influence">Flow of Probabilistic Influence (active trial)</a></li>
<li><a href="https://createmomo.github.io/2019/01/07/Probabilistic-Graphical-Models-Revision-Notes/#independencies">Independencies</a></li>
<li><a href="https://createmomo.github.io/2019/01/07/Probabilistic-Graphical-Models-Revision-Notes/#d_seperation">d-seperation</a></li>
<li><a href="https://createmomo.github.io/2019/01/07/Probabilistic-Graphical-Models-Revision-Notes/#i_maps">I-Maps (Indenpendency Map)</a></li>
<li><a href="https://createmomo.github.io/2019/01/07/Probabilistic-Graphical-Models-Revision-Notes/#factorisation_and_i_maps">Factorisation and I-Maps</a></li>
<li><a href="https://createmomo.github.io/2019/01/07/Probabilistic-Graphical-Models-Revision-Notes/#naive_bayes">Naive Bayes</a></li>
<li><a href="https://createmomo.github.io/2019/01/07/Probabilistic-Graphical-Models-Revision-Notes/#template_models">Template Models</a><ul>
<li><a href="https://createmomo.github.io/2019/01/07/Probabilistic-Graphical-Models-Revision-Notes/#temporal_models">Temporal Models (involve over time)</a></li>
<li><a href="https://createmomo.github.io/2019/01/07/Probabilistic-Graphical-Models-Revision-Notes/#2tbn">2 Time-Slice Bayesian Network (2TBN)</a></li>
<li><a href="https://createmomo.github.io/2019/01/07/Probabilistic-Graphical-Models-Revision-Notes/#plate_models">Plate Models</a></li>
</ul>
</li>
<li><a href="https://createmomo.github.io/2019/01/07/Probabilistic-Graphical-Models-Revision-Notes/#conditional_probability_distribution">Conditional Probability Distribution (CPD)</a><ul>
<li><a href="https://createmomo.github.io/2019/01/07/Probabilistic-Graphical-Models-Revision-Notes/#general_cpd">General CPD</a></li>
<li><a href="https://createmomo.github.io/2019/01/07/Probabilistic-Graphical-Models-Revision-Notes/#table_based_cpd">Table-based CPD</a></li>
<li><a href="https://createmomo.github.io/2019/01/07/Probabilistic-Graphical-Models-Revision-Notes/#context_specific_independence">Context-specific Independence</a></li>
<li><a href="https://createmomo.github.io/2019/01/07/Probabilistic-Graphical-Models-Revision-Notes/#tree_structured_cpd">Tree-Structured CPD</a></li>
<li><a href="https://createmomo.github.io/2019/01/07/Probabilistic-Graphical-Models-Revision-Notes/#multiplexer_cpd">Multiplexer CPD</a></li>
<li><a href="https://createmomo.github.io/2019/01/07/Probabilistic-Graphical-Models-Revision-Notes/#noise_or_cpd">Noise OR CPD</a></li>
<li><a href="https://createmomo.github.io/2019/01/07/Probabilistic-Graphical-Models-Revision-Notes/#sigmoid_cpd">Sigmoid CPD</a></li>
<li><a href="https://createmomo.github.io/2019/01/07/Probabilistic-Graphical-Models-Revision-Notes/#continuous_variables">Continuous Variables</a></li>
</ul>
</li>
</ul>
</li>
<li><strong><a href="https://createmomo.github.io/2019/01/07/Probabilistic-Graphical-Models-Revision-Notes/#markov_network">Markov Network (undirected graph)</a></strong><ul>
<li><a href="https://createmomo.github.io/2019/01/07/Probabilistic-Graphical-Models-Revision-Notes/#markov_network_fundamentals">Markov Network Fundamentals</a><ul>
<li><a href="https://createmomo.github.io/2019/01/07/Probabilistic-Graphical-Models-Revision-Notes/#pairwise_markov_networks">Pairwise Markov Networks</a></li>
<li><a href="https://createmomo.github.io/2019/01/07/Probabilistic-Graphical-Models-Revision-Notes/#general_gibbs_distribution">General Gibbs Distribution (a more general expression)</a></li>
<li><a href="https://createmomo.github.io/2019/01/07/Probabilistic-Graphical-Models-Revision-Notes/#induced_markov_network">Induced Markov Network (connects every pair of nodes that are in the same factor)</a></li>
<li><a href="https://createmomo.github.io/2019/01/07/Probabilistic-Graphical-Models-Revision-Notes/#factorization">Factorization</a></li>
<li><a href="https://createmomo.github.io/2019/01/07/Probabilistic-Graphical-Models-Revision-Notes/#conditional_random_fields">Conditional Random Fields</a></li>
<li><a href="https://createmomo.github.io/2019/01/07/Probabilistic-Graphical-Models-Revision-Notes/#independencies_in_markov_networks">Independencies in Markov Networks</a></li>
</ul>
</li>
<li><a href="https://createmomo.github.io/2019/01/07/Probabilistic-Graphical-Models-Revision-Notes/#local_structure_in_markov_networks">Local Structure in Markov Networks</a><ul>
<li><a href="https://createmomo.github.io/2019/01/07/Probabilistic-Graphical-Models-Revision-Notes/#log_linear_models">Log-linear Models (CRF, Ising Model, Metric MRFs)</a></li>
</ul>
</li>
<li><a href="https://createmomo.github.io/2019/01/07/Probabilistic-Graphical-Models-Revision-Notes/#decision_making">Decision Making</a><ul>
<li><a href="https://createmomo.github.io/2019/01/07/Probabilistic-Graphical-Models-Revision-Notes/#maxium_expected_utility">Maximum Expected Utility</a></li>
<li><a href="https://createmomo.github.io/2019/01/07/Probabilistic-Graphical-Models-Revision-Notes/#utility_functions">Utility Functions</a></li>
<li><a href="https://createmomo.github.io/2019/01/07/Probabilistic-Graphical-Models-Revision-Notes/#value_of_perfect_information">Value of Perfect Information</a></li>
</ul>
</li>
<li><a href="https://createmomo.github.io/2019/01/07/Probabilistic-Graphical-Models-Revision-Notes/#knowledge_engineering">Knowledge Engineering</a><ul>
<li><a href="https://createmomo.github.io/2019/01/07/Probabilistic-Graphical-Models-Revision-Notes/#generative_vs_descriminative">Generative vs. Discriminative</a></li>
<li><a href="https://createmomo.github.io/2019/01/07/Probabilistic-Graphical-Models-Revision-Notes/#designing_a_graphical_model">Designing a graphical model (variable types)</a></li>
<li><a href="https://createmomo.github.io/2019/01/07/Probabilistic-Graphical-Models-Revision-Notes/#structure">Structure</a></li>
<li><a href="https://createmomo.github.io/2019/01/07/Probabilistic-Graphical-Models-Revision-Notes/#parameters_local_structure">Parameters: Local Structure</a></li>
<li><a href="https://createmomo.github.io/2019/01/07/Probabilistic-Graphical-Models-Revision-Notes/#iterative_refinement">Iterative Refinement</a></li>
</ul>
</li>
</ul>
</li>
</ul>
</li>
<li><strong><a href="https://createmomo.github.io/2019/01/07/Probabilistic-Graphical-Models-Revision-Notes/#inference">Inference</a></strong></li>
<li><strong><a href="https://createmomo.github.io/2019/01/07/Probabilistic-Graphical-Models-Revision-Notes/#learning">Learning</a></strong><br><img src="/2019/07/18/Table-of-Contents/dog-pgm-mini.png" alt="The Power of Probabilistic Graphical Models"></li>
</ul>
<p><strong>(Last Updated: 2019.01.06) <a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#tableofcontents">Super Machine Learning Revision Notes</a></strong></p>
<ul>
<li><strong><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#activation_functions">Activation Functions</a></strong></li>
<li><strong><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#gradient_descent">Gradient Descent</a></strong><ul>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#computation_graph">Computation Graph</a></li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#backpropagation">Backpropagation</a></li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#gradients_for_l2_regularization">Gradients for L2 Regularization (weight decay)</a></li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#vanishing_exploding_gradients">Vanishing/Exploding Gradients</a></li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#mini_batch_gradient_descent">Mini-Batch Gradient Descent</a></li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#stochastic_gradient_descent">Stochastic Gradient Descent</a></li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#choosing_mini_batch_size">Choosing Mini-Batch Size</a></li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#gradient_descent_with_momentum">Gradient Descent with Momentum (always faster than SGD)</a></li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#gradient_descent_with_rmsprop">Gradient Descent with RMSprop</a></li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#adam">Adam (put Momentum and RMSprop together)</a></li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#learning_rate_decay_methods">Learning Rate Decay Methods</a></li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#batch_normalization">Batch Normalization</a></li>
</ul>
</li>
<li><strong><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#parameters">Parameters</a></strong><ul>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#learnable_and_hyper_parameters">Learnable and Hyper Parameters</a></li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#parameters_initialization">Parameters Initialization</a></li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#hyper_parameter_tuning">Hyper Parameter Tuning</a></li>
</ul>
</li>
<li><strong><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#regularization">Regularization</a></strong><ul>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#l2_regularization">L2 Regularization (weight decay)</a></li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#l1_regularization">L1 Regularization</a></li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#dropout">Dropout (inverted dropout)</a></li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#early_stopping">Early Stopping</a></li>
</ul>
</li>
<li><strong><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#models">Models</a></strong><ul>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#logistic-regression">Logistic Regression</a></li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#multiclass_classification">Multi-Class Classification (Softmax Regression)</a></li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#transfer_learning">Transfer Learning</a></li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#multitask_learning">Multi-task Learning</a></li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#convolutional_neural_network">Convolutional Neural Network (CNN)</a><ul>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#filter_kernel">Filter/Kernel</a></li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#stride">Stride</a></li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#padding">Padding (valid and same convolutions)</a></li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#a_convolutional_layer">A Convolutional Layer</a></li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#1_1_convolution">1*1 Convolution</a></li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#pooling_layer">Pooling Layer (Max and Average Pooling)</a></li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#lenet_5">LeNet-5</a></li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#alexnet">AlexNet</a></li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#vgg_16">VGG-16 </a></li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#resnet">ResNet (More Advanced and Powerful) </a></li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#inception_network">Inception Network </a></li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#object_detection">Object Detection </a><ul>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#classification_with_localisation">Classification with Localisation</a></li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#landmark_detection">Landmark Detection</a></li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#sliding_windows_detection_algorithm">Sliding Windows Detection Algorithm</a></li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#region_proposal">Region Proposal (R-CNN)</a></li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#yolo_algorithm">YOLO Algorithm</a><ul>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#bounding_box_predictions">Bounding Box Predictions (Basics of YOLO)</a></li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#intersection_over_union">Intersection Over Union</a></li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#non_max_suppression">Non-max Suppression</a></li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#anchor_boxes">Anchor Boxes</a></li>
</ul>
</li>
</ul>
</li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#face_verification">Face Verification</a><ul>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#one_shot_learning">One-Shot Learning (Learning a “similarity” function)</a><ul>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#siamese_network">Siamese Network</a></li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#triplet_loss">Triplet Loss</a></li>
</ul>
</li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#face_recognition_verification_and_binary_classification">Face Recognition/Verification and Binary Classification</a></li>
</ul>
</li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#neural_style_transfer">Neural Style Transfer </a></li>
</ul>
</li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#sequence_models">Sequence Models</a><ul>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#recurrent_neural_network">Recurrent Neural Network Model</a></li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#gated_recurrent_unit">Gated Recurrent Unit (GRU)</a><ul>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#gru_simplified">GRU (Simplified)</a></li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#gru_full">GRU (Full)</a></li>
</ul>
</li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#long_short_term_memory">Long Short Term Memory (LSTM)</a></li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#bidirectional_rnn">Bidirectional RNN</a></li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#deep_rnn_example">Deep RNN Example</a></li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#word_embedding">Word Embedding</a><ul>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#one_hot">One-Hot</a></li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#embedding_matrix">Embedding Matrix ($E$)</a></li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#learning_word_embedding">Learning Word Embedding</a></li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#word2vec_and_skip_gram">Word2Vec &amp; Skip-gram</a></li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#negative_sampling">Negative Sampling</a></li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#glove_vector">GloVe Vector</a></li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#elmo">Deep Contextualized Word Representations (ELMo, Embeddings from Language Models)</a></li>
</ul>
</li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#sequence_to_sequence_model_example">Sequence to Sequence Model Example: Translation</a><ul>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#pick_the_most_likely_sentence">Pick the most likely sentence (Beam Search)</a><ul>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#beam_search">Beam Search</a></li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#length_normalisation">Length Normalisation</a></li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#error_analysis_in_beam_search">Error Analysis in Beam Search (heuristic search algorithm)</a></li>
</ul>
</li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#bleu_score">Bleu Score</a></li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#combined_bleu">Combined Bleu</a></li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#attention_model">Attention Model</a></li>
</ul>
</li>
</ul>
</li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#transformer">Transformer (Attention Is All You Need)</a></li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#bert">Bidirectional Encoder Representations from Transformers (BERT)</a></li>
</ul>
</li>
<li><strong><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#tips">Practical Tips</a></strong><ul>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#train_dev_test">Train/Dev/Test Dataset</a></li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#over_and_under_fitting">Over/UnderFitting, Bias/Variance, Comparing to Human-Level Performance, Solutions</a></li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#mismatched_data_distribution">Mismatched Data Distribution</a></li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#input_normalization">Input Normalization</a></li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#single_number_model_evaluation_metric">Use a Single Number Model Evaluation Metric</a></li>
<li><a href="https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes/#error_analysis">Error Analysis (Prioritize Next Steps)</a></li>
</ul>
</li>
</ul>
<p><img src="/2019/07/18/Table-of-Contents/dog-dl.png" alt="Reviewing..."></p>
<p><strong>[2017] CRF Layer on the Top of BiLSTM (BiLSTM-CRF)</strong></p>
<ul>
<li><a href="https://createmomo.github.io/2017/09/12/CRF_Layer_on_the_Top_of_BiLSTM_1/">CRF Layer on the Top of BiLSTM - 1</a> Outline and Introduction</li>
<li><a href="https://createmomo.github.io/2017/09/23/CRF_Layer_on_the_Top_of_BiLSTM_2/">CRF Layer on the Top of BiLSTM - 2</a> CRF Layer (Emission and Transition Score)</li>
<li><a href="https://createmomo.github.io/2017/10/08/CRF-Layer-on-the-Top-of-BiLSTM-3/">CRF Layer on the Top of BiLSTM - 3</a> CRF Loss Function</li>
<li><a href="https://createmomo.github.io/2017/10/17/CRF-Layer-on-the-Top-of-BiLSTM-4/">CRF Layer on the Top of BiLSTM - 4</a> Real Path Score</li>
<li><a href="https://createmomo.github.io/2017/11/11/CRF-Layer-on-the-Top-of-BiLSTM-5/">CRF Layer on the Top of BiLSTM - 5</a> The Total Score of All the Paths</li>
<li><a href="https://createmomo.github.io/2017/11/24/CRF-Layer-on-the-Top-of-BiLSTM-6/">CRF Layer on the Top of BiLSTM - 6</a> Infer the Labels for a New Sentence</li>
<li><a href="https://createmomo.github.io/2017/12/06/CRF-Layer-on-the-Top-of-BiLSTM-7/">CRF Layer on the Top of BiLSTM - 7</a> Chainer Implementation Warm Up</li>
<li><a href="https://createmomo.github.io/2017/12/07/CRF-Layer-on-the-Top-of-BiLSTM-8/">CRF Layer on the Top of BiLSTM - 8</a> Demo Code</li>
</ul>
<p><img src="/2019/07/18/Table-of-Contents/dog-mini.png" alt="The dog needs to find the best path to get his favorite bone toy and return home following the way he came"></p>
<hr>
<h4 id="My-Life"><a href="#My-Life" class="headerlink" title="My Life"></a><strong><em><a href="https://createmomo.github.io/2018/01/17/My-Life/">My Life</a></em></strong></h4>
      
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